What’s the Difference Between NLU and NLP?

NLP is a broad field that encompasses a wide range of technologies and techniques, while NLU is a subset of NLP that focuses on a specific task. NLG, on the other hand, is a more specialized field that is focused on generating natural language output. Language is how we all communicate and interact, but machines have long lacked the ability to understand human language. NLU can help you save time by automating customer service tasks like answering FAQs, routing customer requests, and identifying customer problems.

NLU can be used to personalize at scale, offering a more human-like experience to customers. For instance, instead of sending out a mass email, NLU can be used to tailor each email to each customer. Or, if you’re using a chatbot, NLU can be used to understand the customer’s intent and provide a more accurate response, instead of a generic one.

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On the other hand, NLU is concerned with comprehending the deeper meaning and intention behind the language. An example of NLU in action is a virtual assistant understanding and responding to a user’s spoken request, such as providing weather information or setting a reminder. NLU can analyze the sentiment or emotion expressed in text, determining whether the sentiment is positive, negative, or neutral.

There are 4.95 billion internet users globally, 4.62 billion social media users, and over two thirds of the world using mobile, and all of them will likely encounter and expect NLU-based responses. Consumers are accustomed to getting a sophisticated reply to their individual, unique input – 20% of Google searches are now done by voice, for example. Without using NLU tools in your business, you’re limiting the customer experience you can provide. Sophisticated contract analysis software helps to provide insights which are extracted from contract data, so that the terms in all your contracts are more consistent.

Benefits of NLU

Natural Language Processing (NLP) holds a transformative power in the business world. The article also details its operations and extensive applications, ranging from sentiment analysis to topic modeling. We also explore NLP’s influence across various industries, including healthcare, banking, and retail, highlighting its capacity to enhance customer interactions and streamline operations. Read on, as this is your essential guide to understanding and leveraging NLP for business success. When all these models are processed together and facilitated with data in voice or text form, it generates intelligent results, and the software becomes capable of understanding human language. In many ways, the difference between NLU and natural language generation (NLG) is the difference between the production of language and comprehension.

Artificial intelligence tools use natural language processing to understand the input of the user. Natural language processing can be a powerful tool for chatbots, helping them understand customer queries and respond accordingly. A good NLP engine can make all the difference between a self-service chatbot that offers a great customer experience and one that frustrates your customers. Natural Language Processing (NLP) has transcended the realm of theory, cementing itself as a transformative technology across various industries. The ability of NLP to interpret and analyze human language brings unparalleled benefits to businesses, enhancing efficiency, understanding, and customer engagement.

NLP (Natural Language Processing)

NLU seeks to identify the underlying intent or purpose behind a given piece of text or speech. It classifies the user’s intention, whether it is a request for information, a command, a question, or an expression of sentiment. Natural Language Understanding in AI aims to understand the context in which language is used. It considers the surrounding words, phrases, and sentences to derive meaning and interpret the intended message. Our brains work hard to understand speech and written text, helping us make sense of the world. Symbolic AI uses human-readable symbols that represent real-world entities or concepts.

This helps in understanding the overall sentiment or opinion conveyed in the text. Language processing begins with tokenization, which breaks the input into smaller pieces. Tokens can be words, characters, or subwords, depending on the tokenization technique.

Improvements in intonation and expressiveness will make synthetic voices seem nearly indistinguishable from real human ones. These advancements will lead to a better user experience with technologies like audiobooks, voiceovers, as well as virtual assistants. Speech AI systems can have a tricky time accurately interpreting languages with various accents of regional variations, making it challenging to ensure that the technology is efficient. Unique linguistic features such as regional dialects or even users speaking more than one language simultaneously can present a challenge. The ability to turn text into speech or vice versa makes technology accessible to more people, particularly those with mobile, visual, or hearing impairments.

For example, speech AI can be employed for transcription services, virtual medical scribes, and voice-activated assistants to streamline workflows and improve patient care. With so many accents, languages, and industry-specific jargon, it can be difficult for a system to pick up on the slight contextual nuances in speech. Not only that, but when we use sarcasm, slang, or subtle variations in our speech or tone, it can affect the accuracy of what a speech AI platform picks up, leading to ambiguous results and outputs.

The complexity of these models varies depending on what type you choose and how much information there is

available about it (i.e., co-occurring words). Statistical models generally don’t rely too heavily on background

knowledge, while machine learning ones do. Still, they’re also more time-consuming to construct and evaluate their

accuracy with new data sets. Sentence breaking refers to the computational process of dividing a sentence into at least two pieces or breaking it up.

• Processing techniques serve as the groundwork upon which understanding techniques are developed and applied.
• In NLU systems, this output is often generated by computer-generated speech or chat interfaces, which mimic human language patterns and demonstrate the system’s ability to process natural language input.
• Whether it’s NLP, NLU, or other AI technologies, our expert team is here to assist you.
• Natural language understanding (NLU) refers to a computer’s ability to understand or interpret human language.
• This provides customers and employees with timely, accurate information they can rely on so that you can focus efforts where it matters most.

NLU recognizes and categorizes entities mentioned in the text, such as people, places, organizations, dates, and more. It helps extract relevant information and understand the relationships between different entities. NER systems scan input text and detect named entity words and phrases using various algorithms. In the statement “Apple Inc. is headquartered in Cupertino,” NER recognizes “Apple Inc.” as an entity and “Cupertino” as a location. Parsing and grammatical analysis help NLP grasp text structure and relationships. Parsing establishes sentence hierarchy, while part-of-speech tagging categorizes words.

Combining computing technologies with human language has become a driving force for modern-day technology. For example, it is difficult for call center employees to remain consistently positive with customers at all hours of the day or night. However, a chatbot can maintain positivity and safeguard your brand’s reputation. By 2025, the NLP market is expected to surpass $43 billion–a 14-fold increase from 2017. Businesses worldwide are already relying on NLU technology to make sense of human input and improved decision-making.

NLG, on the other hand, is above NLU, which can offer more fluidic, engaging, and exciting responses to users as a normal human would give. NLG identifies the essence of the document, and based on those analytics, it generates highly accurate answers. However, with machines, understanding the real meaning behind the provided input isn’t easy to crack. NLU is focused primarily on understanding and interpreting human language, while NLP aims to process and manipulate language in more general terms.

Furthermore, based on specific use cases, we will investigate the scenarios in which favoring one skill over the other becomes more profitable for organizations. This research will provide you with the insights you need to determine which AI solutions are most suited to your organization’s specific needs. The two most common approaches are machine learning and symbolic or knowledge-based AI, but organizations are increasingly using a hybrid approach to take advantage of the best capabilities that each has to offer. Where NLP helps machines read and process text and NLU helps them understand text, NLG or Natural Language Generation helps machines write text. Conversely, NLU focuses on extracting the context and intent, or in other words, what was meant. For those interested, here is our benchmarking on the top sentiment analysis tools in the market.

NLU skills are necessary, though, if users’ sentiments vary significantly or if AI models are exposed to explaining the same concept in a variety of ways. However, NLU lets computers understand “emotions” and “real meanings” of the sentences. You can create your free account now and start building your chatbot right off the bat.

• So, unless you are a software developer specializing in chatbots and AI, you should consider one of the other methods listed below.
• There is a significant difference between NLP and traditional machine learning tasks, with the former dealing with

  unstructured text data while the latter usually deals with structured tabular data.

• In the statement “Apple Inc. is headquartered in Cupertino,” NER recognizes “Apple Inc.” as an entity and “Cupertino” as a location.
• Whether you’re dealing with an Intercom bot, a web search interface, or a lead-generation form, NLU can be used to understand customer intent and provide personalized responses.

Read more about What is the difference between NLP and Use Cases here.